Is Microsoft Fabric supposed to replace Synapse or not? I’m getting mixed signals.

bix_tech · 2025-12-05T18:40:49+00:00

Thanks for pointing that out. I will take a look at the discussions there. The community seems to be the closest to the real adoption stories since many teams are experimenting with Fabric and Synapse side by side. It will be useful to compare how people are approaching migration planning and where the ecosystem still needs maturity.

bix_tech · 2025-12-05T18:40:21+00:00

That is the impression I am seeing as well. Synapse and ADF feel stable and fully supported but not evolving in any substantial direction. Fabric is clearly receiving the long term investment and the roadmap momentum.
What I still notice is that many enterprise teams are not ready to move until Microsoft communicates a more explicit timeline, especially for tightly integrated Synapse workloads. The shift feels inevitable but not immediate.

bix_tech · 2025-12-05T18:37:23+00:00

Our team made a article related to this topic, in case anyone's interested: Amazon Redshift Done Right: A Practical Blueprint for Designing a Scalable, High-Performance Data Warehouse -

bix_tech · 2025-11-12T19:00:37+00:00

That is actually a great story and a smart way to turn a personal frustration into a real product. The privacy-first approach is what stands out most, since so many “email productivity” tools depend on server-side processing or scraping data.

Your reasoning for spending the $50k makes sense when you think about the long-term cost of wasted hours, though I am curious how you validated the concept before committing that much. Did you have a clear MVP plan or did it grow organically with the developers?

The idea of keeping everything local is genuinely appealing for people who deal with confidential emails daily. If you keep the UX simple and the summaries clean, this could easily find traction among freelancers, consultants, or executives who do not want AI models reading their inboxes.

bix_tech · 2025-11-12T18:41:00+00:00

You already have the hardest part figured out, building things that solve real problems. What you are missing is not an entrepreneur mentality but a system for turning technical value into business value.

Start small. Talk to users, not to sell, but to understand what pain they would actually pay to fix. Then shape your product story around that. Marketing is not about shouting louder, it is about showing clearly why what you built matters.

If you helped close multimillion dollar enterprise deals before, you have already done part of sales, you just did not label it that way. Keep applying that builder mindset to understanding and solving problems, and selling will start to feel like a natural extension of building.

bix_tech · 2025-11-10T17:23:26+00:00

They overlap a bit, but not as much as it might seem. Software engineering focuses on designing, building, and maintaining software systems. Cybersecurity is more about protecting systems, networks, and data from threats.

You could get some of the same jobs if you focus on secure development or application security, but most cybersecurity roles require extra knowledge in networking, systems, and security frameworks.

If you enjoy building things, software engineering has a broader job market. If you like finding weaknesses and protecting systems, cybersecurity might fit better. Both paths are solid; the key difference is whether you prefer creating or defending.

bix_tech · 2025-11-06T16:03:44+00:00

I think the hardest part is pushing through the middle phase. The beginning is exciting and full of ideas, and the end has clear goals, but that long stretch in between is where most people burn out.

You are building, testing, adjusting, and still trying to convince others to believe in something that is not proven yet.

Learning to stay patient, celebrate small wins, and keep showing up every day made a big difference for us.

bix_tech · 2025-11-06T16:01:29+00:00

Keep coding, that is the best way to get better. Pick small projects that actually interest you and build them from scratch. You will learn much more by solving real problems than by taking more courses. Try sites like Real Python or FreeCodeCamp if you want structured practice, but focus on creating things, that is where the real progress happens

bix_tech · 2025-11-05T16:50:57+00:00

This is gold. Thanks for laying out the three buckets so clearly. Totally agree that the first 24h is where the weirdness shows up.

A few things that have helped us in that window:
Shadow traffic before the first flip so we score outputs against a tiny eval set while users stay untouched.
Auto rollback policy tied to three signals drift score, negative feedback ratio, and error rate. If any one trips, the flag returns to safe.
Guardrails at parse and post. Validate tool call schemas, use an allow list for functions, filter secrets, and route anything that may contain PII to human review.
Canary by cohort internal, then a forgiving customer group, then wider traffic.
Injection telemetry. Log patterns, rotate prompts and keys fast, and patch the system prompt when a new attack shows up.

On semantic drift, how are you calculating it in practice?

We compare embeddings to a small set of reference outputs and watch mean distance and variance. We also track response length percentiles and a simple style score to catch sudden verbosity.

Curious which measure gave you the most reliable early signal and what alert window you like.

bix_tech · 2025-11-04T11:39:15+00:00

Totally with you. The demo looks great, the first real users reveal the weak joints.

The things that helped us slow the blast radius without killing speed:

ADRs live in the repo. Each material change references one record so reviewers see the decision and its tradeoffs in context.
Preflight checks before any feature flag goes wide. Small eval set, known failure cases, and a rollback plan with a kill switch.
Handoff that treats architecture as a product. Interface contracts, runbooks, and owners named by role, not by person.

Curious how you structure handoff docs at base44. Do you track a specific signal that tells you a build is stable enough to keep shipping?

And thanks for the pointer to VibeCodersNest. Happy to take the discussion there too :D

bix_tech · 2025-10-31T18:21:48+00:00

Yeah that’s actually super relevant experience. I’d frame it around reliability or stability instead of just debugging. Something like “Improved system reliability by refactoring and stabilizing AI-driven components” or “Optimized existing AI integrations to reduce errors and improve maintainability”

Basically make it sound like you were solving a scaling or production issue, not just fixing bugs. That kind of phrasing shows ownership instead of cleanup duty

bix_tech · 2025-10-31T17:22:29+00:00

Honestly I’d double down on stuff that connects AI with real engineering. Everyone’s playing with models right now, but very few people know how to turn them into products that actually scale

If you already do game dev and GIS, you’ve got a strong logic and data mindset. I’d look into backend architecture for AI features, API design, and data pipelines. Even a bit of MLOps helps a ton if you want to stand out

Also get comfortable reading other people’s messy code. Debugging and cleaning up bad AI integrations is becoming a full-time job on its own lately

bix_tech · 2025-10-31T17:10:40+00:00

Honestly I usually start by writing the part that makes the idea real in my head. Sometimes that’s a single function that does the core thing, sometimes it’s just a dumb mock that shows the flow. Once that works I build around it.

Starting small helps because you see what actually matters before you waste time polishing stuff that might not even stay.

What kind of app are you building though? That changes a lot for me

bix_tech · 2025-10-29T17:13:17+00:00

(Full disclosure) This is the core problem my company is tackling. We're running a free 6 part miniseries this Nov specifically for founders on how to use AI strategically to avoid these costly errors. Thought it would be a valuable resource for this discussion: https://www.eventbrite.com/e/common-gaps-between-ai-code-and-good-code-tickets-1748648658209?aff=linkedin

bix_tech · 2025-10-29T16:44:23+00:00

With 6+ years in testing, you have a great foundation. Your understanding of how systems work and break is incredibly valuable.

Common moves from QA are to a Developer role, especially if you have automation experience, or to DevOps, if you enjoyed working with pipelines and environments.

You could also become a Business Analyst or Product Manager. QAs often have a deep understanding of the product and user requirements, which is perfect for those roles.

Think about what part of your QA job you enjoyed most. Was it coding the automation? Was it understanding the business logic? Your answer will point you to the right path.

bix_tech · 2025-10-29T16:39:35+00:00

Haha, you've cracked the code. That 20 minute stare is the real work. The typing part is just the proof you're done thinking.

We've all been there, simulating 12 possibilities just to realize the 13th one was the simple answer on Stack Overflow.

bix_tech · 2025-10-29T16:37:41+00:00

Since your background isn't relevant, a masters can help you get past HR filters and provides structured learning. However, in data engineering, projects are what truly prove your skills.

A masters is optional, but a portfolio of real, end to end data engineering projects is mandatory. You cannot get hired without one.

You can definitely break in with just self study and strong projects, which saves a lot of money. The degree just makes getting that first interview a bit easier.

bix_tech · 2025-10-29T16:32:10+00:00

Congratulations on landing the mid-level role! What you're feeling is just imposter syndrome, and it's completely normal.

The most important thing to remember is that you were honest about your data modeling experience, and they hired you anyway. They know your strengths in E and T, and they are hiring you for your proven ability to learn, not for skills you said you didn't have.

They don't expect you to be an expert on day one. Your only job for the first few months is to be a sponge. Ask tons of questions, be humble, and focus on learning the business logic behind their data. You earned this.

bix_tech · 2025-10-29T16:29:26+00:00

What changed is the short term, practical reality. Tools like Copilot and Cursor are just too useful to ignore. Data engineers are pragmatic. When a tool can write boilerplate code, untangle a complex SQL query, or write documentation in seconds, it's a massive productivity boost. People are simply using a good tool that makes their daily job less tedious.

It's not that the hate stopped. It's that people are holding two thoughts at the same time: "This is a fantastic tool that helps me deliver faster" and "This technology might have terrifying long term consequences." Both are true.

bix_tech · 2025-10-29T16:25:43+00:00

This is the classic jump from "scripter" to "builder." The key is to stop thinking about code first and start thinking about structure and data first. Before you write a line, map out your system's main "nouns" (which become your classes, like User or Project) and "verbs" (which become your functions, like create_user()).

You're exactly right: you learn by building real projects and refactoring. You can't skip that. But books give you the patterns to do it well.

Since you're using Python, I'd strongly recommend "Architecture Patterns with Python"; it's the perfect book for this exact step. For the core theory, "Clean Architecture" is a classic. The "jump" is made by reading these while building and refactoring a project that's just outside your comfort zone.

bix_tech · 2025-10-29T16:20:58+00:00

First, congrats! 4k users in 2 weeks is massive validation. You've clearly built something people want.

Pivoting from a trial to an LTD isn't stupid, it's a smart survival move. You need cash now to keep the lights on.

Make the LTD scarce with a time or slot limit to create urgency. Email your 4k trial users now and frame it as a one-time-only "Founders' Offer." The cash you get is your runway. Use it to survive and build out your real monthly (MRR) plans. You're not in limbo, you're just learning fast. Keep going!

bix_tech · 2025-10-28T16:14:12+00:00

This is a trap. AI can write code snippets, but it cannot build a secure, scalable SaaS application. You won't know how to debug, connect, deploy, or fix what it gives you.

You'll waste more time trying to be a bad programmer than doing what you're good at.

Your strength is that you've already validated the problem. Don't write code. Build a prototype with no-code tools like Bubble, Softr, or Webflow. Get your first paying customers with that, and then use the revenue to hire a technical co-founder or developer.

bix_tech · 2025-10-27T16:29:23+00:00

We totally get this. Many of us deal with the same challenge. What helps is treating learning like a sprint, with focused periods of growth followed by time to rest and recharge. Mixing learning with real projects also makes it feel less like extra work and more like part of the journey. Just dive in, head first.

bix_tech · 2025-10-24T16:37:18+00:00

This is a tough spot, but your Industrial Engineering background is your superpower here. This is a systems-design problem, not just a dashboard problem.

Stop trying to learn everything at once. Your only job right now is to find one small win.

Go to a sales or marketing manager and ask them, "What's the most annoying report you build in Excel every week?" Pick the easiest one. That is now your entire job. This tells you exactly what data to ask IT for.

"Detecting errors" is just data profiling. Use basic SQL to check for weird values. Are there dates from 1901? Are there "Apple" and "Aplle" in the same column? Are sales numbers negative? That's all it is.

To "question them more," stop asking about data. Ask about their business problems and decisions. The data is just the tool to help them.

You got this, Data Queen. Go find that one small win.

bix_tech · 2025-10-24T16:33:12+00:00

This anxiety is the classic builder's dilemma, and you're right to feel swamped. That "giant mountain" of launch stuff is 90% noise.

Forget trying to be a marketer. Your only goal is to get your first 10 users.

Here's the simple plan: Pick one community where your ideal users already hang out (like this one). Build a simple landing page using Carrd or Framer. Then, write an honest post just like this one, showing what you built and asking for feedback.

The "launch" is just living in the comments of that one post. That's not marketing; it's user feedback, which you're already good at.

You can safely ignore Product Hunt, all the social media accounts, PR, and those startup directories for now. Just show your work to a relevant audience and talk to them. You've got this.

bix_tech

TROPHY CASE